Codesign d’une solution en cybersanté : cartographie et dynamique de l’expérience de trois participants
Bibliographic record
Abstract
Le codesign, ou la participation de l'utilisateur visé par un produit ou service à la conception est prometteuse, mais ne garantit pas pour autant l'atteinte des objectifs.Mobilisant les plus récents travaux du cours d'action [THE 15], nous avons examiné l'expérience de trois personnes ayant pris part à des séances de codesign en cybersanté.Notre analyse a permis d'obtenir des cartographies de l'expérience de ces personnes qui présente visuellement la dynamique de leur expérience et souligne ce qui a été significatif de leur point de vue.Nos résultats suggèrent que les participants ont été fortement mobilisés par la réinterprétation des éléments qu'ils croyaient partagés dans leurs communautés.Ils étaient préoccupés par des problèmes systémiques qui, bien que complémentaires, remettaient en question la solution envisagée pour le projet de codesign.Nos résultats nous mènent à proposer trois pistes à explorer pour optimiser la démarche de codesign.ABSTRACT.Codesign, or the involvement of target users in the design of a product or service, is promising but does not guarantee the achievement of objectives.Drawing on the latest work from the course of action [THE 15], we examined the experience of three people who participated in codesign sessions in cyberhealth.Our analysis resulted in maps of these individuals' experiences, which visually represent the dynamics of their experience and highlight what was significant from their point of view.Our results suggest that participants were strongly mobilized by the reinterpretation of elements they believed were shared in their communities.They were concerned with systemic issues that, although complementary, questioned the solution proposed for the cogesign project.Our results lead us to propose three avenues to explore in order to optimize the codesign process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".